activity
20172019
most citedBenchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review

70 citations · 118 across the 6 of their papers we have counts for

collaborators

6 papers

cs.NE2019★ 2 cited

Analysis of the -CSA-ES with Repair by Projection Applied to a Conically Constrained Problem

Patrick Spettel, Hans-Georg Beyer

Theoretical analyses of evolution strategies are indispensable for gaining a deep understanding of their inner workings. For constrained problems, rather simple problems are of int…

cs.NE2018★ 1 cited

Analysis of the --Self-Adaptation Evolution Strategy with Repair by Projection Applied to a Conically Constrained Problem

Patrick Spettel, Hans-Georg Beyer

A theoretical performance analysis of the --Self-Adaptation Evolution Strategy (SA-ES) is presented considering a conically constrained problem. Infeasible offspri…

cs.NE2018

A Linear Constrained Optimization Benchmark For Probabilistic Search Algorithms: The Rotated Klee-Minty Problem

Michael Hellwig, Hans-Georg Beyer

The development, assessment, and comparison of randomized search algorithms heavily rely on benchmarking. Regarding the domain of constrained optimization, the number of currently…

cs.NE2018★ 36 cited

A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints

Patrick Spettel, Hans-Georg Beyer, Michael Hellwig

This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed alg…

cs.NE2018★ 70 cited

Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review

Michael Hellwig, Hans-Georg Beyer

Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas. This paper presen…

cs.NE2017★ 9 cited

Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization

Ilya Loshchilov, Tobias Glasmachers, Hans-Georg Beyer

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popular method to deal with nonconvex and/or stochastic optimization problems when the gradient information is not…